John Hopkins University - Reproducible Research

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Reproducible Research
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Coursera
Overview

Duration	4 hours
Start from	Start Now
Total fee	Free
Mode of learning	Online
Difficulty level	Beginner
Official Website	Explore Free Course
Credential	Certificate

Reproducible Research
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Coursera
Highlights

Earn a Certificate of completion from Johns Hopkins University on successful course completion
Instructors - Roger D. Peng, Jeff Leek, and Brian Caffo
Shareable Certificates
Self-Paced Learning Option

Reproducible Research
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Coursera
Course details

Skills you will learn

R Statistics Data analysis

Who should do this course?

The course is desigend for those who want to learn about concepts and tools behind reporting modern data analyses in a reproducible manner.

More about this course

This course focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them. The need for reproducibility is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary. In addition, reproducibility makes an analysis more useful to others because the data and code that actually conducted the analysis are available. This course will focus on literate statistical analysis tools which allow one to publish data analyses in a single document that allows others to easily execute the same analysis to obtain the same results.

Reproducible Research
at
Coursera
Curriculum

Week 1: Concepts, Ideas, & Structure - This week will cover the basic ideas of reproducible research since they may be unfamiliar to some of you. We also cover structuring and organizing a data analysis to help make it more reproducible. I recommend that you watch the videos in the order that they are listed on the web page, but watching the videos out of order isn't going to ruin the story.

Introduction

What is Reproducible Research About?

Reproducible Research: Concepts and Ideas (part 1)

Reproducible Research: Concepts and Ideas (part 2)

Reproducible Research: Concepts and Ideas (part 3)

Scripting Your Analysis

Structure of a Data Analysis (part 1)

Structure of a Data Analysis (part 2)

Organizing Your Analysis

Week 2: Markdown & knitr - This week we cover some of the core tools for developing reproducible documents. We cover the literate programming tool knitr and show how to integrate it with Markdown to publish reproducible web documents. We also introduce the first peer assessment which will require you to write up a reproducible data analysis using knitr.

Coding Standards in R

Markdown

R Markdown

R Markdown Demonstration

knitr (part 1)

knitr (part 2)

knitr (part 3)

knitr (part 4)

Introduction to Course Project

Week 3: Reproducible Research Checklist & Evidence-based Data Analysis - This week covers what one could call a basic check list for ensuring that a data analysis is reproducible. While it's not absolutely sufficient to follow the check list, it provides a necessary minimum standard that would be applicable to almost any area of analysis.

Communicating Results

RPubs

Reproducible Research Checklist (part 1)

Reproducible Research Checklist (part 2)

Reproducible Research Checklist (part 3)

Evidence-based Data Analysis (part 1)

Evidence-based Data Analysis (part 2)

Evidence-based Data Analysis (part 3)

Evidence-based Data Analysis (part 4)

Evidence-based Data Analysis (part 5)

Week 4: Case Studies & Commentaries - This week there are two case studies involving the importance of reproducibility in science for you to watch.

Caching Computations